What is GUSAR about?


GUSAR is a tool to create models on quantitative structure-activity relationships. The acronym stands for “General Unrestricted Structure-Activity Relationships”.The input of the program is your training set of chemical structures and quantitative data on biological activities. The output is a reliable quantitative SAR/SPR (Structure Activity and Property Relationship) model.

Get a picture of GUSAR

Quantitative prediction of the activity of a chemical compound and its correlation with the known effect with GUSAR (right). The program also assigns to individual atoms whether they are supportive (green) and or suppressive (red) for the effect under consideration (left).
Quantitative prediction of the effect of a chemical compound. Click picture for enlarged view.

Watch this video to gain an impression about GUSAR’s look-and-feel:

Key features

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Unique descriptors and mathematical algorithms
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High speed of predictions
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Easy-to-use interface
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Selection of the most predictive models
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Estimation which parts of a molecule provide positive and negative impact to the activity
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Saving GUSAR output predictions in SDF and CSV formats for subsequent analyses
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Uploading of SD files for batch predictionsNGS data analysis is supported by the platform. ChIP-seq data sets containing in vivo transcription factor binding sites or methylation results can be analyzed with the help of ready-made workflows. Galaxy tools are integrated, supporting RNA-seq data analysis, and many functions more.

Free demo

Download your free trial version of GUSAR:

Please, download and read first the installation instructions (strongly recommended):
How to install GUSAR(pdf, 300 kB).

GUSAR runs under the operating systems Windows® XP / Vista / 7. For the program to install and run, you need about 100 MB free disc space, and at least 1 GB RAM (2 GB or more are recommended).

If all this fits, please download the following zip archive to your local machine (it is a big file, so it may take some time):
GUSAR trial version (zip, 29 MB).

Please, be aware that this version provides you with the full functionality of GUSAR, but will automatically expire after a trial period of 1 week, starting with the installation of the program. Extending the usage then requires a license key, which we will gladly provide when you contact us under info(at)genexplain.com.

In case you have any questions after reading the instructions, or you encounter any problems during the installation, don’t hesitate to contact us under info(at)genexplain.com.

Prices / order

Price information (GUSAR program)

Academic one seat 1-year license

Commercial one seat 1-year license

Free trial version

1000 €

2000 €

0 €

Price information (GUSAR pmodels)
Academic one seat 1-year license

Commercial one seat 1-year license

1000 €

2000 €

Licensing is also possible for 1, 3 or 6 months or for 3 years; please, ask for quotes (info(at)genexplain.com). All prices are without VAT. Appropriate taxes will be added, depending on your location.

GUSAR algorithm

The core of GUSAR consists of a unique algorithm of self-consistent regression that allows to select the best set of descriptors for a robust and reliable QSAR model.
Chemical structures are represented by MNA (Multilevel Neighborhood of Atoms) or QNA (Quantitative Neighbourhoods of Atoms) descriptors and biological activity descriptors that are based on the PASS prediction results for more than 4000 biological activities. QNA descriptors easily reflect the nature of intermolecular interactions. Models developed using biological activity descriptors enable to reveal key mechanisms of action of complex biological effects. MNA and QNA descriptors are used to calculate several variables, such as topological length and volume or lipophilicity of a molecule. For further details, see Filimonov et al. (2009).

GUSAR in comparison

In comparison with a number of 3D and 2D QSAR methods, the predictivity of GUSAR was superior to that of most other QSAR methods both on heterogeneous and on homogeneous data sets.
GUSAR comparison
Comparison of different QSAR approaches; shown is the performance of GUSAR relative to other methods.

Precomputed GUSAR models

Precomputed GUSAR models
Additionally to the GUSAR program, we provide ready-trained GUSAR models to predict certain biological activities. These are SAR bases that can be used with the GUSAR software for predictions on acute rat toxicity, acute mouse toxicity or antitargets (off-targets). Click here to learn more (pdf, 0.6 MB).

GUSAR applications (bibliography)

Information downloads

GUSAR Flyer (download; pdf, 0.35 MB)
GUSAR Presentation (download; pdf, 0.93 MB)
GUSAR Models (download; pdf, 0.54 MB)
See also the GUSAR Homepage of the developers.
Note:
The GUSAR program package is under copyright protection (©) of Zakharov A.V., Filimonov D.A., Poroikov V.V., Lagunin A.A., Russian State Patent Agency Certificate, No. 2006613591 of 15.09.2006.

Publications

Fedorova, E.V., Buryakina, A.V., Zakharov, A.V., Filimonov, D.A., Lagunin, A.A., Poroikov, V.V. (2014) Design, synthesis and pharmacological evaluation of novel vanadium-containing complexes as antidiabetic agents. PLoS One 9:e100386. doi: 10.1371/journal.pone.0100386. PMID: 25057899

Lagunin, A.A., Gloriozova, T.A., Dmitriev, A.V., Volgina, N.E., Poroikov, V.V. (2013) Computer evaluation of drug interactions with P-glycoprotein. Bull. Exp. Biol. Med. 154:521–524. PMID: 23486596.

Zakharov, A.V., Lagunin, A.A., Filimonov, D.A., Poroikov, V.V. (2012) Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem. Res. Toxicol. 25:2378–2385. PMID: 23078046.

Zakharov, A.V., Peach, M.L., Sitzmann, M., Filippov, I.V., McCartney, H.J., Smith, L.H., Pugliese, A., Nicklaus, M.C. (2012) Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med. Chem. 4:1933–1944. PMID: 23088274.

Kokurkina G.V., Dutov M.D., Shevelev S.A., Popkov S.V., Zakharov A.V., Poroikov V.V. (2011) Synthesis, antifungal activity and QSAR study of 2-arylhydroxynitroindoles. Eur. J. Med. Chem. 46:4374–4382. PMID: 21802177.

Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol. Inform. 30:241–250. Link.

Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) QNA based ‘Star Track’ QSAR approach. SAR QSAR Environ. Res. 20:679-709. PMID: 20024804

Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2009). In silico assessment of acute toxicity in rodents. Toxicol. Lett. 189:S264.

Filimonov D.A., Poroikov V.V. (2008) Probabilistic approach in activity prediction. In: Chemoinformatics Approaches to Virtual Screening. Eds. Alexandre Varnek and Alexander Tropsha. Cambridge (UK): RSC Publishing, pp. 182-216.

Filimonov D.A., Poroikov V.V. (2006) Prediction of biological activity spectrum for organic compounds. Russ. Chem. J. 50: 66-75.

Filimonov D.A., Lagunin A.A., Poroikov V.V. (2005) Prediction of activity spectra for substances using new local integrative descriptors. QSAR and Molecular Modelling in Rational Design of Bioactive Molecules. Eds. Esin Aki Sener, Ismail Yalcin, Ankara (Turkey), CADD & D Society, pp. 98-99.

Poroikov V. V., Filimonov D. A., Borodina Yu. V., Lagunin A. A., Kos A. (2000) Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds. J. Chem. Inf. Comput. Sci. 40:1349-1355. PMID: 11128093

Filimonov D., Poroikov V., Borodina Yu., Gloriozova T. (1999) Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms: Definition and Comparison with the Other Descriptors. J. Chem. Inf. Comput. Sci. 39:666-670.Link

Poroikov V. V., Filimonov D. A., Borodina Yu. V., Gloriozova T. A., Sitnikov V. B., Sadovnikov S. V., Sosnov A. V. (2004) Quantitative relationships between structure and delayed neurotoxicity of chemicals studied by the Self-Consistent Regression method using the PASS program. Pharmaceutical Chemistry Journal 38:188-190.

Filimonov D. A., Akimov D. V., Poroikov V. V. (2004) The method of Self-Consistent Regression for the quantitative analysis of relationships between structure and properties of chemicals. Pharmaceutical Chemistry Journal 38:21-24.

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